Other data

Widen your viewpoint with other omics data.

Statistical bioinformatics methods can be applied to any set of biological data, not just sequencing. In addition to individual data sources, integration of multiple data types allows access to knowledge that would be impossible to gather from any one type of dataset alone.

Read more about our data analysis below:

Quantitation of proteins using parallel mass spectrometers is an increasingly popular method of studying gene expression within cells and excretion of proteins to extracellular space. We can run statistical analyses on proteomics data in order to identify differentially expressed or excreted proteins, for example.

Large numbers of biological samples have been characterized using gene expression, gene copy number, and SNP arrays, with new samples continuously being studied. We have extensive experience in analysis of all microarray types, and would be happy to help you with re-analysis of your old data, or analysis of new arrays. Generally, from microarray studies we deliver results comparable to all applicable analyses listed for genomics, transcriptomics, and epigenomics.

Extraction of information from chemical or physiological systems results in biochemical signals. We apply signal processing and multivariate statistical methods to transform these signals into meaningful biological quantities. Multiple signals can be integrated and the resulting data used as an input for correlative methods in order to understand their clinical relevance, or for machine learning methods in order to build predictive systems.

High-throughput imaging assays can result in large quantities of data that are laborious to analyze by hand. We can apply image processing algorithms in order to identify relevant features from image data and convert them into easily analyzable quantities for further statistical analysis.

Informatics challenges in biology are not always limited to quantitative molecular data -understanding textual data can be a relevant problem too. For example, integrating, classifying, storing and visualizing sample meta-data from extensive collections, such as biobanks, is a prerequisite for their use. We can apply informatics approaches that organize such data into coherent databases and allow visualization of the content. After assuring proper organization, we can then run association and machine learning algorithms in order to make unexpected discoveries from your archives.

Indicative features - genetic or other types - can be revealed by statistically comparing samples of interest to a control group. We can use genomic, transcriptomic and epigenomic data with metadata in order to find a biomarker (or a combination of biomarkers) that can be used to classify future samples into relevant categories, such as patients likely to respond to a treatment versus non-responders.

Panels of classifying genetic features that differentiate between known groups or unexpected subgroups (such as cancer subgroups) can be found using statistical methods. We can analyze these features further, using pathway analysis for example, in order to understand the implications of these molecular differences.

In order to model the gene regulatory network pertinent to a pathological or normal condition, we can infer the regulatory interactions from a time-series RNA-sequencing experiment. Transcription factor binding site prediction or ChIP experiments can be used to support the modelling task. Furthermore, the model can be constructed so as to enable simulations, shedding light on the network functionality.

Insight into some biological systems can be gained by applying mathematical modelling schemes, such as differential equation models for cellular signalling pathways. We can suggest an appropriate modelling strategy if we think it will help you to gain information relevant to your research question.

Accumulated patient data enable the creation of nomograms that can be used to assess disease prognosis or risks for clinical operations. For example, a nomogram could be used to estimate the risk of nodal metastasis of a tumor. The prediction is based on clinical and/or biological parameters, such as patient age, data on symptoms and treatment, genetic variants, gene expression levels, or other biomarkers. Depending on the number of parameters used, the nomogram can be implemented as a simple mathematical formula, MS Excel™ spreadsheet or an interactive online tool that will allow easy and widespread usage.

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